Many data analysis tasks deal with multiple datasets. Geovisual analytics are the data analysis methodwhich deals with geospatial datasets. Geovisual analytics focused on the significant knowledge discoveryamong its participating datasets. Representing geospatial datasets on a geographical map is a naivemanner in geovisual analytics approaches. In many cases, data points can be grouped as events based onseveral criteria such the same time, location, subject. Presenting events in an interactive geographical mapmay help an analyst to discover knowledge more effectively and efficiently than presenting them asdiscrete data points. While working with multiple datasets, each dataset may come from differentconceptual entities or may come from the same conceptual entity. This nature of the data sources leads thedata analysis in two different directions, analysis of anomalies and analysis of similarities. When multipledatasets comes from same conceptual entities, it is expected that the datasets will have similarities. As aresult, the dissimilarity among them will be of interest to the analyst. In contrast, similarity will be ofinterest for datasets originate from different conceptual entities. Only few works are found which dealswith the event based analysis of multiple geospatial datasets. Also a very little work is found deals withthe anomaly analysis. This research intends to explore geovisual analytics approaches that consider theevents as the units of analysis. In particular, the research will develop geovisual analytics tools and studyhow they can be used to support event based analysis of similarities and anomalies among multiplegeospatial datasets.